Value-based purchasing is a quality improvement strategy that links payment with healthcare outcomes, by paying less or not at all for poor outcomes. The Centers for Medicare and Medicaid Services (CMS) seeks to decrease the rate of hospital-acquired complications (HAC) and readmissions by holding hospitals financially accountable using risk-adjusted rates. Current CMS risk-adjustment models for readmission include patient characteristics from routine administrative data (e.g. age, gender, diagnosis codes). Research suggests other important patient characteristics such as functional status, mobility and level of social support also impact patients' risk for readmission and certain complications (e.g., pressure ulcers). To date, variables such as functional status, mobility and social support have not been evaluated for or included in risk-adjustment models because they are not available in typical administrative data. To address knowledge gaps regarding the impact of functional status, mobility and social support on complication events and readmission rates, we will utilize a unique data source - the nationally representative Health and Retirement Study (HRS) (20) (as a gold standard data source for these type of patient-specific measures) linked to patient-specific Medicare claims data, by 4 specific aims: 1. To assess change in performance of our recently constructed risk-adjusted claims-based model for complications of pressure ulcers and urinary tract infections as HACs after enhancement with HRS patient-specific measures of functional status, mobility, and social support. 2. To assess change in performance of CMS's current risk-adjustment models for readmission (for pneumonia, heart failure, myocardial infarction) after enhancement with HRS patient-specific measures of functional status, mobility, and social support. 3. To evaluate the potential of census-derived, ZIP-code level measures of disability, mobility, and household support, as surrogates for the HRS patient-specific measures to enhance CMS risk- adjustment models for HACs and readmission. 4. To evaluate the policy impact of using (or not using) measures of patient functional status, mobility and social support upon risk-adjusted hospital rates of readmissions and complications in state level claims- based data, by two approaches: 1) using related census-derived ZIP-code level measures, and 2) modeling in claims data the impact of differential distribution seen in the HRS data of the patient- specific measures by hospital types (e.g., safety-net, teaching, public hospitals). This research is vital to the science of quality assessment and expansion of value-based purchasing programs to avoid unfair penalties to hospitals that care for vulnerable patients with high intrinsic risk for readmissions and complications.